Opioid abuse has become an epidemic with devastating health, social, and economic consequences for the United States. Drug overdose deaths have nearly tripled from 1999?2014. During 2015, drug overdoses accounted for 52,404 U.S. deaths, including 33,091 (63.1%) that involved an opioid (opioid pain relievers and heroin). Opioid analgesic overdose is now the leading cause of injury death in the U.S; in 2014, there were approximately one and a half times more drug overdose deaths than deaths from motor vehicle accidents. Treatment for an opioid use disorder (OUD) is a complex multifactorial process often involving medication assisted treatment (MAT) and requiring changes in deeply imbedded behaviors. Patients are treated in the clinic, but fight addiction in their daily lives. Rates of relapse are high, and access to supportive care is limited and costly. Treatment approaches must be tailored to address each patient?s drug use patterns and drugrelated medical, psychiatric, and social problems. While a few online tools exist to assist patients in their recovery, current products offer only generalized information that may or may not be relevant and timely, or act solely as supportive social networking services. In previous work, we developed KIOS, an innovative software platform derived from nonlinear control theory. KIOS tracks multiple interacting symptoms to map patient trajectories and deliver evidence-based intervention strategies responsive to the specific needs of individuals. Accessible via computer or mobile devices, KIOS provides patients real time advice and reinforcement of lifestyle interventions to improve selfmanagement. KIOS also can provide between-visit reporting to clinicians via downloadable reports or direct online monitoring. Therefore, the goal of this project is to adapt KIOS to patients with an opioid use disorder. The development of this innovative tool to help patients manage OUD has the potential to have a profound impact on public health and achieve significant commercial success. This Phase I SBIR study has two Specific Aims: Specific Aim 1: Identify Patient Trajectories of OUD and Link Expert Advice to Changing Patient States Specific Aim 2: Build User Interfaces and Test Feasibility in Patients with OUD